18.11.2024 - Building a Recommender Model for the Online Betting Shop Gamingtec
Building a Recommender Model for the Online Betting Shop Gamingtec Recommender Systems integrated within an Online Betting Shop presents a particularly intriguing area of study: there is almost no research in this field, and devising an appropriate cost function to optimize poses a non-trivial challenge. In this talk, I will explore recommender models that cater to the needs of both the platform owners and their end users. Attendees will gain insights into the challenges faced in designing and implementing such a system for Gamingtec.
Machine Learning Week Europe 2024, Dr. Mirza Klimenta
25.06.2024 - Graph-based Recommender Systems - when to use them
Recommender System models continue to grow in complexity, yet some relatively straightforward approaches, which might provide better solutions for most businesses, are often overlooked. In this talk, I will delve deeper into recommender models based on graph theory and graph algorithms, ranging from PageRank walks to Graph Neural Networks. This exploration will include an evaluation of their practical applications and effectiveness in various business contexts.
22.04.2024 - Content Recommendation with Graphs: From Basic Walks to Neural Networks
Discover how graph algorithms are transforming content recommendation in this insightful talk. We'll journey from the basics of graph-based models, exploring simple graph walks, to the cutting-edge realm of Graph Neural Networks. Uncover the power of graph embeddings and learn when graph-based approaches excel in recommender systems.
A Recommender System for an Audio-on-Demand Platform
Building an effective recommender system for Audio-on-Demand platforms presents unique challenges.Beyond the user-item interaction data, the inclusion of user and item metadata holds promise for enhancing recommendation quality. However, when faced with limited availability of metadata, can we still construct a robust recommender? This thought-provoking talk delves into the realm of recommender systems, exploring how simple collaborative filtering techniques can be adapted to accommodate scarce metadata. Furthermore, we explore an intriguing alternative approach: treating user-item interactions as interconnected nodes within a graph. By leveraging graph-based methodologies, we unveil a powerful graph-based recommender system. Through a captivating use-case, we highlight the potential of this novel viewpoint, elucidating its ability to uncover hidden patterns and enhance recommendation accuracy.
Graph Neural Networks for Predicting Stock Market Shares at StockFink
Explore the potential of graph algorithms, i.e. Graph Neural Networks (GNN), in predicting stock market prices. This approach, involving the analysis of relationships between hedge funds and companies, goes beyond single graphs to utilize a series of graphs for deeper insight. Think about link prediction, and link-weight prediction. Join us for insights and takeaways that may inform your next investment strategy, already a part of StockFink's predictive arsenal.
